{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import glob\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensorflow version: 2.0.0-rc0\n"
     ]
    }
   ],
   "source": [
    "print('Tensorflow version: {}'.format(tf.__version__))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_image_path = glob.glob('./flower_photos/*/*.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_image_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['./dc/train\\\\cat\\\\cat.0.jpg',\n",
       " './dc/train\\\\cat\\\\cat.1.jpg',\n",
       " './dc/train\\\\cat\\\\cat.10.jpg',\n",
       " './dc/train\\\\cat\\\\cat.100.jpg',\n",
       " './dc/train\\\\cat\\\\cat.101.jpg']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_image_path[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['./dc/train\\\\dog\\\\dog.995.jpg',\n",
       " './dc/train\\\\dog\\\\dog.996.jpg',\n",
       " './dc/train\\\\dog\\\\dog.997.jpg',\n",
       " './dc/train\\\\dog\\\\dog.998.jpg',\n",
       " './dc/train\\\\dog\\\\dog.999.jpg']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_image_path[-5:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "p = './dc/train\\\\dog\\\\dog.995.jpg'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "int(p.split('\\\\')[1] == 'cat')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_image_label = [int(p.split('\\\\')[1] == 'cat') for p in train_image_path]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 0, 0, 0, 0]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_image_label[-5:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_preprosess_image(path, label):\n",
    "    image = tf.io.read_file(path)\n",
    "    image = tf.image.decode_jpeg(image, channels=3)\n",
    "    image = tf.image.resize(image, [360, 360])\n",
    "    image = tf.image.random_crop(image, [256, 256, 3])\n",
    "    image = tf.image.random_flip_left_right(image)\n",
    "    image = tf.image.random_flip_up_down(image)\n",
    "    image = tf.image.random_brightness(image, 0.5)\n",
    "    image = tf.image.random_contrast(image, 0, 1)\n",
    "    image = tf.cast(image, tf.float32)\n",
    "    image = image/255\n",
    "    label = tf.reshape(label, [1])\n",
    "    return image, label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "#[1, 2, 3]  -->  [[1], [2], [3]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "#tf.image.convert_image_dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_image_ds = tf.data.Dataset.from_tensor_slices((train_image_path, train_image_label))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "AUTOTUNE = tf.data.experimental.AUTOTUNE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_image_ds = train_image_ds.map(load_preprosess_image, num_parallel_calls=AUTOTUNE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ParallelMapDataset shapes: ((256, 256, 3), (1,)), types: (tf.float32, tf.int32)>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_image_ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for img, label in train_image_ds.take(1):\n",
    "    plt.imshow(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "BATCH_SIZE = 32\n",
    "train_count = len(train_image_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_image_ds = train_image_ds.shuffle(train_count).batch(BATCH_SIZE)\n",
    "train_image_ds = train_image_ds.prefetch(AUTOTUNE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_image_path = glob.glob('./dc/test/*/*.jpg')\n",
    "test_image_label = [int(p.split('\\\\')[1] == 'cat') for p in test_image_path]\n",
    "test_image_ds = tf.data.Dataset.from_tensor_slices((test_image_path, test_image_label))\n",
    "test_image_ds = test_image_ds.map(load_preprosess_image, num_parallel_calls=AUTOTUNE)\n",
    "test_image_ds = test_image_ds.batch(BATCH_SIZE)\n",
    "test_image_ds = test_image_ds.prefetch(AUTOTUNE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1000"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(test_image_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TensorShape([32, 256, 256, 3])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imgs.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TensorShape([32, 1])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2223bbb99e8>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(imgs[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: id=75, shape=(1,), dtype=int32, numpy=array([1])>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = keras.Sequential([\n",
    "    tf.keras.layers.Conv2D(64, (3, 3), input_shape=(256, 256, 3), activation='relu'),\n",
    "    tf.keras.layers.BatchNormalization(),\n",
    "    tf.keras.layers.Conv2D(64, (3, 3), input_shape=(256, 256, 3), activation='relu'),\n",
    "    tf.keras.layers.BatchNormalization(),\n",
    "    tf.keras.layers.MaxPooling2D(),\n",
    "    tf.keras.layers.Conv2D(128, (3, 3), activation='relu'),\n",
    "    tf.keras.layers.BatchNormalization(),\n",
    "    tf.keras.layers.Conv2D(128, (3, 3), activation='relu'),\n",
    "    tf.keras.layers.BatchNormalization(),\n",
    "    tf.keras.layers.MaxPooling2D(),\n",
    "    tf.keras.layers.Conv2D(256, (3, 3), activation='relu'),\n",
    "    tf.keras.layers.BatchNormalization(),\n",
    "    tf.keras.layers.Conv2D(256, (3, 3), activation='relu'),\n",
    "    tf.keras.layers.BatchNormalization(),\n",
    "    tf.keras.layers.MaxPooling2D(),\n",
    "     tf.keras.layers.BatchNormalization(),\n",
    "    tf.keras.layers.Conv2D(512, (3, 3), activation='relu'),\n",
    "    tf.keras.layers.Conv2D(512, (3, 3), activation='relu'),\n",
    "    tf.keras.layers.MaxPooling2D(),\n",
    "    tf.keras.layers.Conv2D(1024, (3, 3), activation='relu'),\n",
    "    tf.keras.layers.Conv2D(1024, (3, 3), activation='relu'),\n",
    "    tf.keras.layers.GlobalAveragePooling2D(),\n",
    "    tf.keras.layers.Dense(256, activation='relu'),\n",
    "    tf.keras.layers.Dense(1)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d (Conv2D)              (None, 254, 254, 64)      1792      \n",
      "_________________________________________________________________\n",
      "batch_normalization (BatchNo (None, 254, 254, 64)      256       \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 252, 252, 64)      36928     \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 252, 252, 64)      256       \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 126, 126, 64)      0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 124, 124, 128)     73856     \n",
      "_________________________________________________________________\n",
      "batch_normalization_2 (Batch (None, 124, 124, 128)     512       \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 122, 122, 128)     147584    \n",
      "_________________________________________________________________\n",
      "batch_normalization_3 (Batch (None, 122, 122, 128)     512       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 61, 61, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 59, 59, 256)       295168    \n",
      "_________________________________________________________________\n",
      "batch_normalization_4 (Batch (None, 59, 59, 256)       1024      \n",
      "_________________________________________________________________\n",
      "conv2d_5 (Conv2D)            (None, 57, 57, 256)       590080    \n",
      "_________________________________________________________________\n",
      "batch_normalization_5 (Batch (None, 57, 57, 256)       1024      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 28, 28, 256)       0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_6 (Batch (None, 28, 28, 256)       1024      \n",
      "_________________________________________________________________\n",
      "conv2d_6 (Conv2D)            (None, 26, 26, 512)       1180160   \n",
      "_________________________________________________________________\n",
      "conv2d_7 (Conv2D)            (None, 24, 24, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 12, 12, 512)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_8 (Conv2D)            (None, 10, 10, 1024)      4719616   \n",
      "_________________________________________________________________\n",
      "conv2d_9 (Conv2D)            (None, 8, 8, 1024)        9438208   \n",
      "_________________________________________________________________\n",
      "global_average_pooling2d (Gl (None, 1024)              0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 256)               262400    \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1)                 257       \n",
      "=================================================================\n",
      "Total params: 19,110,465\n",
      "Trainable params: 19,108,161\n",
      "Non-trainable params: 2,304\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = model(imgs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TensorShape([32, 1])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.array([p[0].numpy() for p in tf.cast(pred > 0, tf.int32)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.array([l[0].numpy() for l in labels])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ls = tf.keras.losses.BinaryCrossentropy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ls([0.,0.,1.,1.], [1.,1.,1.,1.])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ls([[0.],[0.],[1.],[1.]], [[1.],[1.],[1.],[1.]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tf.keras.losses.binary_crossentropy([0.,0.,1.,1.], [1.,1.,1.,1.])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = tf.keras.optimizers.Adam()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "epoch_loss_avg = tf.keras.metrics.Mean('train_loss')\n",
    "train_accuracy = tf.keras.metrics.Accuracy()\n",
    "\n",
    "epoch_loss_avg_test = tf.keras.metrics.Mean('test_loss')\n",
    "test_accuracy = tf.keras.metrics.Accuracy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: id=145, shape=(), dtype=float32, numpy=0.6666667>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_accuracy([1,0,1], [1,1,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_step(model, images, labels):\n",
    "    with tf.GradientTape() as t:\n",
    "        pred = model(images)\n",
    "        loss_step = tf.keras.losses.BinaryCrossentropy(from_logits=True)(labels, pred)\n",
    "    grads = t.gradient(loss_step, model.trainable_variables)\n",
    "    optimizer.apply_gradients(zip(grads, model.trainable_variables))\n",
    "    epoch_loss_avg(loss_step)\n",
    "    train_accuracy(labels, tf.cast(pred>0, tf.int32))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_step(model, images, labels):\n",
    "    pred = model(images, trianing=False)\n",
    "    loss_step = tf.keras.losses.BinaryCrossentropy(from_logits=True)(labels, pred)\n",
    "    epoch_loss_avg_test(loss_step)\n",
    "    test_accuracy(labels, tf.cast(pred>0, tf.int32))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_loss_results = []\n",
    "train_acc_results = []\n",
    "\n",
    "test_loss_results = []\n",
    "test_acc_results = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_epochs = 30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Logging before flag parsing goes to stderr.\n",
      "W0816 23:25:12.084194 12868 deprecation.py:323] From c:\\users\\guanghua\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\tensorflow\\python\\ops\\nn_impl.py:182: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "...."
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-41-dc37e9d72a14>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mepoch\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnum_epochs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mimgs_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabels_\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mtrain_image_ds\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m         \u001b[0mtrain_step\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mimgs_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabels_\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m         \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'.'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mend\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m''\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-38-855f106f5cb7>\u001b[0m in \u001b[0;36mtrain_step\u001b[1;34m(model, images, labels)\u001b[0m\n\u001b[0;32m      3\u001b[0m         \u001b[0mpred\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimages\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m         \u001b[0mloss_step\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkeras\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlosses\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mBinaryCrossentropy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfrom_logits\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpred\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m     \u001b[0mgrads\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgradient\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mloss_step\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtrainable_variables\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      6\u001b[0m     \u001b[0moptimizer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_gradients\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgrads\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtrainable_variables\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m     \u001b[0mepoch_loss_avg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mloss_step\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\guanghua\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\tensorflow\\python\\eager\\backprop.py\u001b[0m in \u001b[0;36mgradient\u001b[1;34m(self, target, sources, output_gradients, unconnected_gradients)\u001b[0m\n\u001b[0;32m   1000\u001b[0m         \u001b[0moutput_gradients\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0moutput_gradients\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1001\u001b[0m         \u001b[0msources_raw\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mflat_sources_raw\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1002\u001b[1;33m         unconnected_gradients=unconnected_gradients)\n\u001b[0m\u001b[0;32m   1003\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1004\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_persistent\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\guanghua\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\tensorflow\\python\\eager\\imperative_grad.py\u001b[0m in \u001b[0;36mimperative_grad\u001b[1;34m(tape, target, sources, output_gradients, sources_raw, unconnected_gradients)\u001b[0m\n\u001b[0;32m     74\u001b[0m       \u001b[0moutput_gradients\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     75\u001b[0m       \u001b[0msources_raw\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 76\u001b[1;33m       compat.as_str(unconnected_gradients.value))\n\u001b[0m",
      "\u001b[1;32mc:\\users\\guanghua\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\tensorflow\\python\\eager\\backprop.py\u001b[0m in \u001b[0;36m_gradient_function\u001b[1;34m(op_name, attr_tuple, num_inputs, inputs, outputs, out_grads, skip_input_indices)\u001b[0m\n\u001b[0;32m    135\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mnum_inputs\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    136\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 137\u001b[1;33m   \u001b[1;32mreturn\u001b[0m \u001b[0mgrad_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmock_op\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0mout_grads\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    138\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    139\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\guanghua\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\tensorflow\\python\\ops\\nn_grad.py\u001b[0m in \u001b[0;36m_Conv2DGrad\u001b[1;34m(op, grad)\u001b[0m\n\u001b[0;32m    594\u001b[0m           \u001b[0mexplicit_paddings\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mexplicit_paddings\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    595\u001b[0m           \u001b[0muse_cudnn_on_gpu\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0muse_cudnn_on_gpu\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 596\u001b[1;33m           data_format=data_format),\n\u001b[0m\u001b[0;32m    597\u001b[0m       gen_nn_ops.conv2d_backprop_filter(\n\u001b[0;32m    598\u001b[0m           \u001b[0mop\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\guanghua\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\tensorflow\\python\\ops\\gen_nn_ops.py\u001b[0m in \u001b[0;36mconv2d_backprop_input\u001b[1;34m(input_sizes, filter, out_backprop, strides, padding, use_cudnn_on_gpu, explicit_paddings, data_format, dilations, name)\u001b[0m\n\u001b[0;32m   1475\u001b[0m         \u001b[1;34m\"use_cudnn_on_gpu\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0muse_cudnn_on_gpu\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"padding\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpadding\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1476\u001b[0m         \u001b[1;34m\"explicit_paddings\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexplicit_paddings\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"data_format\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata_format\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1477\u001b[1;33m         \"dilations\", dilations)\n\u001b[0m\u001b[0;32m   1478\u001b[0m       \u001b[1;32mreturn\u001b[0m \u001b[0m_result\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1479\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0m_core\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_FallbackException\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "for epoch in range(num_epochs):\n",
    "    for imgs_, labels_ in train_image_ds:\n",
    "        train_step(model, imgs_, labels_)\n",
    "        print('.', end='')\n",
    "    print()\n",
    "    \n",
    "    train_loss_results.append(epoch_loss_avg.result())\n",
    "    train_acc_results.append(train_accuracy.result())\n",
    "    \n",
    "    \n",
    "    for imgs_, labels_ in test_image_ds:\n",
    "        test_step(model, imgs_, labels_)\n",
    "        \n",
    "    test_loss_results.append(epoch_loss_avg_test.result())\n",
    "    test_acc_results.append(test_accuracy.result())\n",
    "    \n",
    "    print('Epoch:{}: loss: {:.3f}, accuracy: {:.3f}, test_loss: {:.3f}, test_accuracy: {:.3f}'.format(\n",
    "        epoch + 1,\n",
    "        epoch_loss_avg.result(),\n",
    "        train_accuracy.result(),\n",
    "        epoch_loss_avg_test.result(),\n",
    "        test_accuracy.result()\n",
    "    ))\n",
    "    \n",
    "    epoch_loss_avg.reset_states()\n",
    "    train_accuracy.reset_states()\n",
    "    \n",
    "    epoch_loss_avg_test.reset_states()\n",
    "    test_accuracy.reset_states()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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